import os, argparse



import tensorflow as tf



# The original freeze_graph function

# from tensorflow.python.tools.freeze_graph import freeze_graph 



dir = os.path.dirname(os.path.realpath(__file__))



def freeze_graph(model_dir, output_node_names):

    """Extract the sub graph defined by the output nodes and convert 

    all its variables into constant 



    Args:

        model_dir: the root folder containing the checkpoint state file

        output_node_names: a string, containing all the output node's names, 

                            comma separated

    """

    if not tf.gfile.Exists(model_dir):

        raise AssertionError(

            "Export directory doesn't exists. Please specify an export "

            "directory: %s" % model_dir)



    if not output_node_names:

        print("You need to supply the name of a node to --output_node_names.")

        return -1



    # We retrieve our checkpoint fullpath

    checkpoint = tf.train.get_checkpoint_state(model_dir)

    input_checkpoint = checkpoint.model_checkpoint_path

    

    # We precise the file fullname of our freezed graph

    absolute_model_dir = "/".join(input_checkpoint.split('/')[:-1])

    output_graph = absolute_model_dir + "/frozen_model.pb"



    # We clear devices to allow TensorFlow to control on which device it will load operations

    clear_devices = True



    # We start a session using a temporary fresh Graph

    with tf.Session(graph=tf.Graph()) as sess:

        # We import the meta graph in the current default Graph

        saver = tf.train.import_meta_graph(input_checkpoint + '.meta', clear_devices=clear_devices)



        # We restore the weights

        saver.restore(sess, input_checkpoint)



        # We use a built-in TF helper to export variables to constants

        output_graph_def = tf.graph_util.convert_variables_to_constants(

            sess, # The session is used to retrieve the weights

            tf.get_default_graph().as_graph_def(), # The graph_def is used to retrieve the nodes 

            output_node_names.split(",") # The output node names are used to select the usefull nodes

        ) 



        # Finally we serialize and dump the output graph to the filesystem

        with tf.gfile.GFile(output_graph, "wb") as f:

            f.write(output_graph_def.SerializeToString())

        print("%d ops in the final graph." % len(output_graph_def.node))



    return output_graph_def



if __name__ == '__main__':

    parser = argparse.ArgumentParser()

    parser.add_argument("--model_dir", type=str, default="", help="Model folder to export")

    parser.add_argument("--output_node_names", type=str, default="", help="The name of the output nodes, comma separated.")

    args = parser.parse_args()



    freeze_graph(args.model_dir, args.output_node_names)
